class_docstr: Crops the image `src` and `bbox` to the given `crop`.
  
  Parameters
  ----------
  crop_box : tuple
      Tuple of length 4. :math:`(x_{min}, y_{min}, width, height)`
  allow_outside_center : bool
      If `False`, remove bounding boxes which have centers outside cropping area.
  
  
  Inputs:
      - **data**: input tensor with (Hi x Wi x C) shape.
      - **bbox**: input tensor with shape (N, 4+) where N is the number of bounding boxes.
          The second axis represents attributes of the bounding box.
          Specifically, these are :math:`(x_{min}, y_{min}, x_{max}, y_{max})`,
          we allow additional attributes other than coordinates, which stay intact
          during bounding box transformations.
  
  Outputs:
      - **out**: output tensor with (H x W x C) shape.
      - **bbox**: output tensor with shape (M, 4+) where M <= N is the number of valid bounding
          boxes after cropping. :math:`(x_{min}, y_{min}, x_{max}, y_{max})`
code: def forward(self, img, bbox):
      if self.xmax >= img.shape[-2] or self.ymax >= img.shape[-3]:
          return img, bbox
      if is_np_array():
          new_img = npx.image.crop(
              img, self.xmin, self.ymin, self.width, self.height
          )
          new_bbox = np.array(
              bbox_crop(
                  bbox.asnumpy(),
                  (self.xmin, self.ymin, self.width, self.height),
                  self._allow_outside_center,
              )
          )
      else:
          new_img = nd.image.crop(
              img, self.xmin, self.ymin, self.width, self.height
          )
          new_bbox = nd.array(
              bbox_crop(
                  bbox.asnumpy(),
                  (self.xmin, self.ymin, self.width, self.height),
                  self._allow_outside_center,
              )
          )
      return new_img, new_bbox
  
imports: import random
  from .......base import numeric_types
  from ......block import Block
  from .......util import is_np_array
  from .......None import ndarray, numpy_extension, numpy
  from .utils import _check_bbox_shape, bbox_crop, bbox_translate
  from .utils import bbox_resize, bbox_random_crop_with_constraints
label_desc: Use image node_id 2 for calling the ImageBboxCrop obj's underlying member method code with example usage: obj.forward(img, bbox) and returns: new_img,  new_bbox, img,  bbox